24,022 research outputs found
Nonlinear Adaptive Control Using Gaussian Networks with Composite Adaptation for Improved Convergence
The use of composite adaptive laws for control of the affine class of nonlinear systems having unknown dynamics is proposed. These dynamics are approximated by Gaussian radial basis function neural network whose parameters are updated by a composite law that is driven by both tracking and estimation errors, combining techniques used in direct and indirect adaptive control. This is motivated by the need to improve the speed of convergence of the unknown parameters, hence resulting in a better system performance. The inherent approximation error of the neural networks might lead to instability because of parameter drift. This is compensated for by augmenting the control law with a low gain sliding mode component and using deadzone adaptation for the indirect part of the composite law, The stability of the system is analysed and the effectiveness of the method is demonstrated by simulation and comparison with a direct adaptive control scheme
Online Hybrid Intelligent Tracking Control for Uncertain Nonlinear Dynamical Systems
[[abstract]]A novel online hybrid direct/indirect adaptive Petri fuzzy neural network (PFNN) controller with stare observer for a class of multi-input multi-output (MIMO) uncertain nonlinear systems is developed in the paper. By using the Lyapunov synthesis approach, the online observer-based tracking control law and the weight-update law of the adaptive hybrid intelligent controller are derived. According to the importance and viability of plant knowledge and control knowledge, a weighting factor is utilized to sum together the direct and indirect adaptive PFNN controllers. In this paper, we prove that the proposed online observer-based hybrid PFNN controller can guarantee that all signals involved are bounded and that the system outputs of the closed-loop system can track asymptotically the desired output trajectories. An example including four cases is illustrated to show the effectiveness of this approach.[[conferencetype]]ćé[[conferencedate]]20120918~20120922[[booktype]]é»ćç[[iscallforpapers]]Y[[conferencelocation]]Tokyo, Japa
Control of Complex Dynamic Systems by Neural Networks
This paper considers the use of neural networks (NN's) in controlling a nonlinear, stochastic system with unknown process equations. The NN is used to model the resulting unknown control law. The approach here is based on using the output error of the system to train the NN controller without the need to construct a separate model (NN or other type) for the unknown process dynamics. To implement such a direct adaptive control approach, it is required that connection weights in the NN be estimated while the system is being controlled. As a result of the feedback of the unknown process dynamics, however, it is not possible to determine the gradient of the loss function for use in standard (back-propagation-type) weight estimation algorithms. Therefore, this paper considers the use of a new stochastic approximation algorithm for this weight estimation, which is based on a 'simultaneous perturbation' gradient approximation that only requires the system output error. It is shown that this algorithm can greatly enhance the efficiency over more standard stochastic approximation algorithms based on finite-difference gradient approximations
Composite Learning Control With Application to Inverted Pendulums
Composite adaptive control (CAC) that integrates direct and indirect adaptive
control techniques can achieve smaller tracking errors and faster parameter
convergence compared with direct and indirect adaptive control techniques.
However, the condition of persistent excitation (PE) still has to be satisfied
to guarantee parameter convergence in CAC. This paper proposes a novel model
reference composite learning control (MRCLC) strategy for a class of affine
nonlinear systems with parametric uncertainties to guarantee parameter
convergence without the PE condition. In the composite learning, an integral
during a moving-time window is utilized to construct a prediction error, a
linear filter is applied to alleviate the derivation of plant states, and both
the tracking error and the prediction error are applied to update parametric
estimates. It is proven that the closed-loop system achieves global
exponential-like stability under interval excitation rather than PE of
regression functions. The effectiveness of the proposed MRCLC has been verified
by the application to an inverted pendulum control problem.Comment: 5 pages, 6 figures, conference submissio
Neural Modeling and Control of Diesel Engine with Pollution Constraints
The paper describes a neural approach for modelling and control of a
turbocharged Diesel engine. A neural model, whose structure is mainly based on
some physical equations describing the engine behaviour, is built for the
rotation speed and the exhaust gas opacity. The model is composed of three
interconnected neural submodels, each of them constituting a nonlinear
multi-input single-output error model. The structural identification and the
parameter estimation from data gathered on a real engine are described. The
neural direct model is then used to determine a neural controller of the
engine, in a specialized training scheme minimising a multivariable criterion.
Simulations show the effect of the pollution constraint weighting on a
trajectory tracking of the engine speed. Neural networks, which are flexible
and parsimonious nonlinear black-box models, with universal approximation
capabilities, can accurately describe or control complex nonlinear systems,
with little a priori theoretical knowledge. The presented work extends optimal
neuro-control to the multivariable case and shows the flexibility of neural
optimisers. Considering the preliminary results, it appears that neural
networks can be used as embedded models for engine control, to satisfy the more
and more restricting pollutant emission legislation. Particularly, they are
able to model nonlinear dynamics and outperform during transients the control
schemes based on static mappings.Comment: 15 page
Data-driven adaptive model-based predictive control with application in wastewater systems
This study is concerned with the development of a new data-driven adaptive model-based predictive controller (MBPC) with input constraints. The proposed methods employ subspace identification technique and a singular value decomposition (SVD)-based optimisation strategy to formulate the control algorithm and incorporate the input constraints. Both direct adaptive model-based predictive controller (DAMBPC) and indirect adaptive model-based predictive controller (IAMBPC) are considered. In DAMBPC, the direct identification of controller parameters is desired to reduce the design effort and computational load while the IAMBPC involves a two-stage process of model identification and controller design. The former method only requires a single QR decomposition for obtaining the controller parameters and uses a receding horizon approach to process input/output data for the identification. A suboptimal SVD-based optimisation technique is proposed to incorporate the input constraints. The proposed techniques are implemented and tested on a fourth order non-linear model of a wastewater system. Simulation results are presented to compare the direct and indirect adaptive methods and to demonstrate the performance of the proposed algorithms
Neural self-tuning adaptive control of non-minimum phase system
The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity, if not unstable, closed-loop behavior. Therefore, a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response
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